AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation. The AI Applications Architect is the senior technical leader responsible for designing and delivering enterprise-grade architectures for agentic AI solutions across AHEAD’s client portfolio.
Requirements
- 6+ years designing and delivering cloud-native, event-driven, or distributed architectures at scale (AWS/Azure).
- Deep hands-on experience with: Kubernetes/EKS, Docker, Terraform, and cloud infrastructure patterns
- Deep hands-on experience with: Python, FastAPI, async frameworks, serverless APIs
- Deep hands-on experience with: Vector DBs (Pinecone, Elasticsearch, pgvector) and RAG/LLM integration workflows
- Deep hands-on experience with: Agentic AI frameworks (LangChain, LangGraph, Autogen, CrewAI, LlamaIndex)
- Strong knowledge of security, identity, devsecops pipelines, and secrets management in cloud environments.
- Experience operating LLMs/SLMs in production (NIMs, Bedrock, OpenAI, Azure OpenAI).
Responsibilities
- Design and own cloud-native architectures (AWS/Azure) for agentic AI workloads using Kubernetes/EKS, Terraform, Docker, serverless APIs, AWS Batch, and async orchestration frameworks (Celery, Step Functions, EventBridge, StoneBranch).
- Define agentic system patterns using LangChain, LangGraph, Autogen, LlamaIndex, Pinecone, and other multi-agent frameworks; ensure consistency of prompt/tool design, memory/state handling, and workflow orchestration.
- Architect vector database, RAG, embeddings pipelines, and model-serving endpoints (LLM/SLM) with strong emphasis on scalability and latency management.
- Establish platform-wide standards for API gateway patterns, identity and auth (OAuth2, Cognito, Vault), secrets management, event contracts/schemas, and data governance.
- Ensure holistic observability across multi-agent systems: tracing, metrics, logging, SLO/SLA definitions, synthetic checks, and incident response playbooks.
- Lead architecture reviews, threat modeling, and performance benchmarking for agentic workloads.
- Champion automation, IaC, CI/CD, model deployment workflows, runbooks, and platform governance.
Other
- Partner with Product Owners, engineering leads, and client stakeholders to translate strategic goals into scalable designs and implementation roadmaps.
- Guide engineering teams through architectural decisions, distributed design principles, and production-readiness standards.
- Mentor engineers in Kubernetes/EKS, async programming, multi-agent orchestration, cloud-native development, and responsible AI practices.
- Provide input on hiring, onboarding, and talent development to grow AHEAD’s agentic engineering bench.
- Partner with Delivery Leads to ensure architecture is executable, scalable, and aligned with timelines.
